Artificial neural networks are powerful information processors conceptually based on the field of neurobiology as is well known in the art. Such networks strive to simulate biological neural network functions in computer architectures. In other words, an artificial neural network must correlate a wide variety of inputs to produce an output.
Recently, self-organizing neural network systems have been introduced for the field of pattern classification. Briefly, a self-organizing neural network is one that may be trained in an unsupervised fashion with a set of arbitrary sequences of training patterns. The neural network actively regulates the learning process to establish a set of output exemplar groupings of related training patterns. Each exemplar grouping has its boundaries governed by a specified tolerance or vigilance parameter as it is known. Once trained, the neural network processes a random input pattern to select one exemplar grouping that is most closely related to the input pattern. A more detailed description of this theory may be found in Adaptive Pattern Recognition and Neural Networks, Chapter 7: "Self-Organizing Nets for Pattern Recognition", by Yoh-Han Pao, Addison-Wesley, 1989. One implementation of a self-organizing neural network for pattern recognition is disclosed in U.S. Pat. No. 4,914,708 issued to Carpenter et al. on Apr. 3, 1990. However, prior art realization of such neural network pattern classification systems utilize an internal probability threshold to resolve how an input pattern will be classified. Accordingly, while the system output does yield a classification of the input pattern as it relates to the trained set of exemplar groupings, there is no degree of quality or confidence associated with the choice made by the neural network.